{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "krtRyhJ3TrC_"
   },
   "source": [
    "## Importing Packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "B6FuNK65fTHP"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0q_lkwWwT6m7"
   },
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "id": "eyl5eh33fd3e",
    "outputId": "6fb4c9fb-3412-4d96-e552-4e8beb578c14"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SpiceJet</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>09:00</td>\n",
       "      <td>11:25</td>\n",
       "      <td>2h 25m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>18:55</td>\n",
       "      <td>10:25 13 Mar</td>\n",
       "      <td>15h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>08:00</td>\n",
       "      <td>05:05 02 Mar</td>\n",
       "      <td>21h 5m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>22270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>08:55</td>\n",
       "      <td>10:25 13 Mar</td>\n",
       "      <td>25h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>27/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>11:25</td>\n",
       "      <td>19:15</td>\n",
       "      <td>7h 50m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination  \\\n",
       "0             IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "1          Air India       1/05/2019   Kolkata    Banglore   \n",
       "2        Jet Airways       9/06/2019     Delhi      Cochin   \n",
       "3             IndiGo      12/05/2019   Kolkata    Banglore   \n",
       "4             IndiGo      01/03/2019  Banglore   New Delhi   \n",
       "5           SpiceJet      24/06/2019   Kolkata    Banglore   \n",
       "6        Jet Airways      12/03/2019  Banglore   New Delhi   \n",
       "7        Jet Airways      01/03/2019  Banglore   New Delhi   \n",
       "8        Jet Airways      12/03/2019  Banglore   New Delhi   \n",
       "9  Multiple carriers      27/05/2019     Delhi      Cochin   \n",
       "\n",
       "                   Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "0              BLR → DEL    22:20  01:10 22 Mar   2h 50m    non-stop   \n",
       "1  CCU → IXR → BBI → BLR    05:50         13:15   7h 25m     2 stops   \n",
       "2  DEL → LKO → BOM → COK    09:25  04:25 10 Jun      19h     2 stops   \n",
       "3        CCU → NAG → BLR    18:05         23:30   5h 25m      1 stop   \n",
       "4        BLR → NAG → DEL    16:50         21:35   4h 45m      1 stop   \n",
       "5              CCU → BLR    09:00         11:25   2h 25m    non-stop   \n",
       "6        BLR → BOM → DEL    18:55  10:25 13 Mar  15h 30m      1 stop   \n",
       "7        BLR → BOM → DEL    08:00  05:05 02 Mar   21h 5m      1 stop   \n",
       "8        BLR → BOM → DEL    08:55  10:25 13 Mar  25h 30m      1 stop   \n",
       "9        DEL → BOM → COK    11:25         19:15   7h 50m      1 stop   \n",
       "\n",
       "               Additional_Info  Price  \n",
       "0                      No info   3897  \n",
       "1                      No info   7662  \n",
       "2                      No info  13882  \n",
       "3                      No info   6218  \n",
       "4                      No info  13302  \n",
       "5                      No info   3873  \n",
       "6  In-flight meal not included  11087  \n",
       "7                      No info  22270  \n",
       "8  In-flight meal not included  11087  \n",
       "9                      No info   8625  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df=pd.read_excel(\"/content/drive/My Drive/flight fare pediction/Data_Train.xlsx\")\n",
    "df = pd.read_excel(\"data/data_train.xlsx\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dbu_s_0iULIH"
   },
   "source": [
    "## Data Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "id": "UN9-V71UAaX0",
    "outputId": "ffeeb284-9385-4b0f-abbb-397a0d966680"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "id": "RMgf9wNAHeOm",
    "outputId": "1e47b8ee-cc40-4b06-f495-296e22899693"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T-kvkT91UUJZ"
   },
   "source": [
    "## Filtering Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "id": "einq44JyAv3j",
    "outputId": "6db1d153-b33c-47e1-bb21-82d3e7f5bd81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "id": "L-QbgBawA3Ik",
    "outputId": "9cda1dd2-f70d-4745-8832-104b629db910",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding Unique Values and Eccoding\n",
    "For better understanding of Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Total_Stops.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      5625\n",
       "non-stop    3491\n",
       "2 stops     1520\n",
       "3 stops       45\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Total_Stops.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Source.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Destination.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IndiGo', 'Air India', 'Jet Airways', 'SpiceJet',\n",
       "       'Multiple carriers', 'GoAir', 'Vistara', 'Air Asia',\n",
       "       'Vistara Premium economy', 'Jet Airways Business',\n",
       "       'Multiple carriers Premium economy', 'Trujet'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Airline.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No info', 'In-flight meal not included',\n",
       "       'No check-in baggage included', '1 Short layover', 'No Info',\n",
       "       '1 Long layover', 'Change airports', 'Business class',\n",
       "       'Red-eye flight', '2 Long layover'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Additional_Info.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         8344\n",
       "In-flight meal not included     1982\n",
       "No check-in baggage included     320\n",
       "1 Long layover                    19\n",
       "Change airports                    7\n",
       "Business class                     4\n",
       "No Info                            3\n",
       "2 Long layover                     1\n",
       "Red-eye flight                     1\n",
       "1 Short layover                    1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Additional_Info'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional Info is not a good distribution for training \n",
    "So we will remove this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Additional_Info',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop  13302  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding for unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.fit(df.Airline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Airline'] = encoder.transform(df.Airline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Air Asia', 'Air India', 'GoAir', 'IndiGo', 'Jet Airways',\n",
       "       'Jet Airways Business', 'Multiple carriers',\n",
       "       'Multiple carriers Premium economy', 'SpiceJet', 'Trujet',\n",
       "       'Vistara', 'Vistara Premium economy'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  1,  4,  8,  6,  2, 10,  0, 11,  5,  7,  9])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Airline'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Source'] = encoder.fit_transform(df['Source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banglore', 'Chennai', 'Delhi', 'Kolkata', 'Mumbai'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Destination'] = encoder.fit_transform(df['Destination'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banglore', 'Cochin', 'Delhi', 'Hyderabad', 'Kolkata', 'New Delhi'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
       "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Total_Stops.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting Total Stops to number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_Stops'] = df.Total_Stops.apply(lambda x:'0 stop' if x=='non-stop' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_Stops'] = df.Total_Stops.apply(lambda x:int(x.split()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline Date_of_Journey  Source  Destination                  Route  \\\n",
       "0        3      24/03/2019       0            5              BLR → DEL   \n",
       "1        1       1/05/2019       3            0  CCU → IXR → BBI → BLR   \n",
       "2        4       9/06/2019       2            1  DEL → LKO → BOM → COK   \n",
       "3        3      12/05/2019       3            0        CCU → NAG → BLR   \n",
       "4        3      01/03/2019       0            5        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration  Total_Stops  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m            0   3897  \n",
       "1    05:50         13:15   7h 25m            2   7662  \n",
       "2    09:25  04:25 10 Jun      19h            2  13882  \n",
       "3    18:05         23:30   5h 25m            1   6218  \n",
       "4    16:50         21:35   4h 45m            1  13302  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working With Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date_of_Journey'] =pd.to_datetime(df['Date_of_Journey'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10682 entries, 0 to 10682\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   Airline          10682 non-null  int32         \n",
      " 1   Date_of_Journey  10682 non-null  datetime64[ns]\n",
      " 2   Source           10682 non-null  int32         \n",
      " 3   Destination      10682 non-null  int32         \n",
      " 4   Route            10682 non-null  object        \n",
      " 5   Dep_Time         10682 non-null  object        \n",
      " 6   Arrival_Time     10682 non-null  object        \n",
      " 7   Duration         10682 non-null  object        \n",
      " 8   Total_Stops      10682 non-null  int64         \n",
      " 9   Price            10682 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int32(3), int64(2), object(4)\n",
      "memory usage: 792.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dep_time to dep_time hour of the day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Dep_Time'] = df.Dep_Time.apply(lambda x:int(x.split(':')[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "same for arriving time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Arrival_Time'] = df.Arrival_Time.apply(lambda x:int(x.split(':')[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjustments with time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Arrival_Time'] = df[['Dep_Time','Arrival_Time']].apply(lambda x:x['Arrival_Time']+24 if x['Dep_Time']>x['Arrival_Time'] else x['Arrival_Time'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: - Arrival time is shown as Dep_time + Duration Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>19h</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline Date_of_Journey  Source  Destination                  Route  \\\n",
       "0        3      2019-03-24       0            5              BLR → DEL   \n",
       "1        1      2019-01-05       3            0  CCU → IXR → BBI → BLR   \n",
       "2        4      2019-09-06       2            1  DEL → LKO → BOM → COK   \n",
       "3        3      2019-12-05       3            0        CCU → NAG → BLR   \n",
       "4        3      2019-01-03       0            5        BLR → NAG → DEL   \n",
       "\n",
       "   Dep_Time  Arrival_Time Duration  Total_Stops  Price  \n",
       "0        22            25   2h 50m            0   3897  \n",
       "1         5            13   7h 25m            2   7662  \n",
       "2         9            28      19h            2  13882  \n",
       "3        18            23   5h 25m            1   6218  \n",
       "4        16            21   4h 45m            1  13302  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Month of journey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month_of_Journey'] =  df['Date_of_Journey'].map(lambda x:x.month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Duration in minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def duration_time(x):\n",
    "    x = x.split()\n",
    "    x = list(map(lambda t:int(t[:-1]),x))\n",
    "    if len(x) == 1:\n",
    "        return x[0]*60\n",
    "    else:\n",
    "        return x[0]*60 + x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Duration'] =  df['Duration'].apply(duration_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Month_of_Journey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>445</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>1140</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>285</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline Date_of_Journey  Source  Destination                  Route  \\\n",
       "0        3      2019-03-24       0            5              BLR → DEL   \n",
       "1        1      2019-01-05       3            0  CCU → IXR → BBI → BLR   \n",
       "2        4      2019-09-06       2            1  DEL → LKO → BOM → COK   \n",
       "3        3      2019-12-05       3            0        CCU → NAG → BLR   \n",
       "4        3      2019-01-03       0            5        BLR → NAG → DEL   \n",
       "\n",
       "   Dep_Time  Arrival_Time  Duration  Total_Stops  Price  Month_of_Journey  \n",
       "0        22            25       170            0   3897                 3  \n",
       "1         5            13       445            2   7662                 1  \n",
       "2         9            28      1140            2  13882                 9  \n",
       "3        18            23       325            1   6218                12  \n",
       "4        16            21       285            1  13302                 1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cc8c4b760>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'],kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see output data is not Distributed Uniformally\n",
    "To make it uniform we will remove data of price > 40000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Month_of_Journey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>5</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>52229</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>54826</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2618</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>375</td>\n",
       "      <td>1</td>\n",
       "      <td>54826</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>5</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>340</td>\n",
       "      <td>1</td>\n",
       "      <td>79512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5372</th>\n",
       "      <td>5</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>62427</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5439</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>54826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7351</th>\n",
       "      <td>5</td>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DEL → ATQ → BOM → COK</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>46490</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9715</th>\n",
       "      <td>5</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DEL → ATQ → BOM → COK</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>52285</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10364</th>\n",
       "      <td>5</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>BLR → MAA → DEL</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>57209</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey  Source  Destination                  Route  \\\n",
       "657          5      2019-01-03       0            5        BLR → BOM → DEL   \n",
       "1478         4      2019-03-18       0            5        BLR → BOM → DEL   \n",
       "2618         4      2019-03-18       0            5        BLR → BOM → DEL   \n",
       "2924         5      2019-01-03       0            5        BLR → BOM → DEL   \n",
       "5372         5      2019-01-03       0            5        BLR → BOM → DEL   \n",
       "5439         4      2019-01-03       0            5        BLR → BOM → DEL   \n",
       "7351         5      2019-03-03       2            1  DEL → ATQ → BOM → COK   \n",
       "9715         5      2019-06-03       2            1  DEL → ATQ → BOM → COK   \n",
       "10364        5      2019-01-03       0            5        BLR → MAA → DEL   \n",
       "\n",
       "       Dep_Time  Arrival_Time  Duration  Total_Stops  Price  Month_of_Journey  \n",
       "657           5            10       300            1  52229                 1  \n",
       "1478         18            24       365            1  54826                 3  \n",
       "2618         22            29       375            1  54826                 3  \n",
       "2924          5            11       340            1  79512                 1  \n",
       "5372          5            12       400            1  62427                 1  \n",
       "5439         16            23       365            1  54826                 1  \n",
       "7351         20            28       500            2  46490                 3  \n",
       "9715         20            28       500            2  52285                 6  \n",
       "10364         9            14       280            1  57209                 1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Price']>40000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['Price']>40000].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cc91029a0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'],kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Better than Before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inputs for Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Month_of_Journey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>3897</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>445</td>\n",
       "      <td>2</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>1140</td>\n",
       "      <td>2</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>285</td>\n",
       "      <td>1</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline  Source  Destination  Dep_Time  Arrival_Time  Duration  \\\n",
       "0        3       0            5        22            25       170   \n",
       "1        1       3            0         5            13       445   \n",
       "2        4       2            1         9            28      1140   \n",
       "3        3       3            0        18            23       325   \n",
       "4        3       0            5        16            21       285   \n",
       "\n",
       "   Total_Stops  Price  Month_of_Journey  \n",
       "0            0   3897                 3  \n",
       "1            2   7662                 1  \n",
       "2            2  13882                 9  \n",
       "3            1   6218                12  \n",
       "4            1  13302                 1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df._get_numeric_data().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df._get_numeric_data().drop('Price',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting of Data in the ratio of 70% , 30% for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model  = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cc69afca0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cc693ed00>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((y_test-predictions),bins=50,kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Looking at the visualization we can tell that scatterplot fits around a Straight line .\n",
    " - It means that Model is not evaluating randomly \n",
    " - And It works Good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2417.9705040478357\n",
      "MSE: 10202567.004940853\n",
      "RMSE: 3194.145739464756\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output:\n",
    "- This model is predicting values of the fare with an error of 2400 rs.\n",
    "- This Output also contains price above 35000 so it's not a bad prediction"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "fligth fare prediction .ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
